[Development of Dose Calculation Application for Computed Tomography Dose Management When Facility Protocols Differ from Diagnostic Reference Level (DRL)].

PURPOSE: In this study, we aimed to develop an application that computes dose values resembling diagnostic reference level (DRL) conditions when disparity prevents direct dose comparisons between the national diagnostic reference levels in Japan 2020 (Japan DRLs_2020) and facility-specific computed tomography (CT) protocols. METHODS: We developed an application using the R programming language and RStudio software that computes dose values and median values based on Japan DRLs_2020 imaging conditions following extraction of necessary information for dose calculations from the Radiation Dose Structured Report (RDSR) and Digital Imaging and Communications in Medicine (DICOM) tags. To ensure a user-friendly experience, we used the Shiny package to develop a graphical user interface that enables the application to operate seamlessly in web browsers. RESULTS: The developed application successfully facilitated the calculation of dose and median values that aligned with the Japan DRLs_2020 for protocols whose imaging range and acquisition timing differed from those of the Japan DRLs_2020. CONCLUSION: By calculating dose values that align with DRL conditions, our application contributes to the implementation and optimization of dose management in CT for facilities that use diverse imaging protocols.

[Display Performance of X-ray Image Output from DICOM-embedded Printer to Paper-based Hard Copy Print: Comparison with X-ray Image on LCD].

PURPOSE: We have been using a paper-based hard copy print (paper print) system of X-ray images, in which digital imaging and communications in medicine (DICOM) data can be directly output on papers from medical imaging systems or from a picture archiving and communication system (PACS) server, and they are utilized as patient referral materials or for preoperative planning. The purpose of this study was to compare the display performance of X-ray images on the printed paper and that on the liquid crystal display (LCD). METHODS: We measured contrast response to verify consistency of image appearance on both display systems. The contrast resolution was assessed by a CDRAD phantom. The spatial resolution was assessed by an X-ray test chart. RESULTS: The contrast response of the paper printer was not concordant with the grayscale standard display function (GSDF). The difference between the measured contrast response and the ideal GSDF on the paper was large in the high-density area. The low-contrast resolution on the paper was inferior to that on the LCD. The spatial resolving power on the paper was superior to that on the LCD. CONCLUSION: The display performance of the paper printer for X-ray images was clarified. X-ray images printed on the paper should be used carefully taking account of their characteristics of display performance.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

An approach for the online determination of Entrance Surface Air Kerma (ESAK) from Kerma Area product (PKA) in digital radiology.

We propose determining the entrance surface air kerma (ESAK) from the kerma area product (PKA) in digital radiology. ESAK values were derived from the X-ray tube outputs and patient exposure factors across five X-ray departments. Using linear regression between ESAK and PKA values, the slope and intercept coefficients were determined for each X-ray equipment and procedure. The method was examined using the data collected from patients who underwent chest PA/LAT, abdomen, pelvic AP, and lumbar spine AP/LAT X-ray examinations. The results showed a highly significant correlation between ESAK and PKA values and correlation coefficients, ranging from 0.77 to 1 with P-value < 0.001 in most studies. This method can be employed by incorporating dose data and related parameters into the X-ray device's software, similar to other dose-displayed information. The online determination of ESAK from PKA could help with quality assurance and patient dose management in digital radiology.

Securing Your Radiology Practice: Evidence-Based Strategies for Radiologists Compiled From 10 Years of Cyberattacks and HIPAA Breaches Involving Medical Imaging.

While there is significant literature discussing physical and cybersecurity risks around health information technology in general, the number of publications that specifically address medical imaging is much smaller, and many of these focus on the technical security requirements for the exchange of medical images over public networks rather than practical guidelines for radiologists and technicians. This study examines the US Department of Health and Human Services database of reported breaches involving medical imaging from 2010-2020, identifies the most common contributing factors to those breaches, and offers recommendations for radiology practices to prevent each, based on the National Institute of Standards and Technology (NIST) guidelines as well as measures proposed in the literature on health information technology.

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals' PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners' examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.

Understanding Scanner Utilization With Real-Time DICOM Metadata Extraction.

Understanding system performance metrics ensures better utilization of the radiology resources with more targeted interventions. The images produced by radiology scanners typically follow the DICOM (Digital Imaging and Communications in Medicine) standard format. The DICOM images consist of textual metadata that can be used to calculate key timing parameters, such as the exact study durations and scanner utilization. However, hospital networks lack the resources and capabilities to extract the metadata from the images quickly and automatically compute the scanner utilization properties. Thus, they resort to using data records from the Radiology Information Systems (RIS). However, data acquired from RIS are prone to human errors, rendering many derived key performance metrics inadequate and inaccurate. Hence, there is motivation to establish a real-time image transfer from the Picture Archiving and Communication Systems (PACS) to receive the DICOM images from the scanners to research clusters to conduct such metadata processing to evaluate scanner utilization metrics efficiently and quickly. This paper analyzes the scanners' utilization by developing a real-time monitoring framework that retrieves radiology images into a research cluster using the DICOM networking protocol and then extracts and processes the metadata from the images. Our proposed approach facilitates a better understanding of scanner utilization across a vast healthcare network by observing properties such as study duration, the interval between the encounters, and the series count of studies. Benchmarks against using the RIS data indicate that our proposed framework based on real-time PACS data estimates the scanner utilization more accurately. Furthermore, our framework has been running stable and performing its computation for more than two years on our extensive healthcare network in pseudo real-time.

Carotid Artery Plaque Identification and Display System (MRI-CAPIDS) Using Opensource Tools.

Magnetic resonance imaging (MRI) represents one modality in atherosclerosis risk assessment, by permitting the classification of carotid plaques into either high- or low-risk lesions. Although MRI is generally used for observing the impact of atherosclerosis on vessel lumens, it can also show both the size and composition of itself, as well as plaque information, thereby providing information beyond that of simple stenosis. Software systems are a valuable aid in carotid artery stenosis assessment wherein commercial software is readily available but is not accessible to all practitioners because of its often high cost. This study focuses on the development of a software system designed entirely for registration, marking, and 3D visualization of the wall and lumen, using freely available open-source tools and libraries. It was designed to be free from "feature bloat" and avoid "feature-creep." The image loading and display module of the modified QDCM library was improved by a minimum of 10,000%. A Bezier function was used in order to smoothen the curve of the polygon (referring to the shape formed by the marked points) by interpolating additional points between the marked points. This smoother curve led to a smoother 3D view of the lumen and wall.

Free DICOM-Viewers for Veterinary Medicine : Survey and Comparison of Functionality and User-Friendliness of Medical Imaging PACS-DICOM-Viewer Freeware for Specific Use in Veterinary Medicine Practices.

There is increasing prevalence of digital diagnostic imaging in veterinary medicine with a progressive need to use medical imaging software. As Digital Imaging and Communications in Medicine (DICOM)-viewers for veterinary use do not require medical device approval in many countries, freeware viewers might be a practical alternative. The aim of this study was to identify and evaluate free DICOM-viewer software for veterinary purposes. The functionality and user-friendliness of various DICOM-viewers from the internet were analyzed and compared. Inclusion criteria for the evaluation were free availability, PACS (picture archiving and communication system)-connectivity, and stand-alone and client-based software. Based on this, eight viewers were found: Ginkgo CADx, Horos, K-PACS, MAYAM, MITO, OsiriX Lite, RadiAnt, Synedra personal. In these DICOM-viewers, 14 core tools were tested and rated on a score from 1 to 10 by multiple observers with different levels of training, using studies of four imaging modalities. Criteria were functionality and user-friendliness. For each viewer, the total number of a predefined set of 47 important tools was counted. The ranking based on functionality and user-friendliness of 14 core tools (mean score in brackets) was as follows: 1. Horos/OsiriX Lite (8.96), 2. RadiAnt (8.90), 3. K-PACS (8.02), 4. Synedra (7.43), 5. MAYAM (6.05), 6. Ginkgo CADx (5.53), 7. MITO (3.74). The DICOM-viewers offered between 20 and 44 tools of the predefined important tool set and are sufficient for most veterinary purposes. An increasing number of tools did not necessarily impair user-friendliness, if the user interface is well designed. Based on the results of this study, veterinarians will find suitable free DICOM-viewers for their individual needs. In combination with PACS-freeware, this allows veterinary practices to run a low-budget digital imaging environment.

Towards an Information Infrastructure for Medical Image Sharing.

Information infrastructures involve the notion of a shared, open infrastructure, constituting a space where people, organizations, and technical components associate to develop an activity. The current infrastructure for medical image sharing, based on PACS/DICOM technologies, does not constitute an information infrastructure since it is limited in its ability to share in a scalable, comprehensive, and secure manner. This paper proposes the DICOMFlow, a decentralized, distributed infrastructure model that aims to foment the formation of an information infrastructure in order to share medical images and teleradiology. As an installed base, it uses the PACS/DICOM infrastructure of radiology departments and the internet e-mail infrastructure. Experiments performed in real and simulated environments have indicated the feasibility of the proposed infrastructure to foment the formation of an information infrastructure for medical image sharing and teleradiology.

Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations.

In order to successfully share patient data across multiple systems, a reliable method of linking patient records across disparate organizations is required. In Canada, within the province of Ontario, there are four centralized diagnostic imaging repositories (DIRs) that allow multiple hospitals and independent health facilities (IHF) to send diagnostic images and reports for the purpose of sharing patient data across the region (Nagels et al. J Digit Imaging 28: 188, 2015). In 2017, the opportunity to consolidate the two regional DIRs that share the south-central and southeast area of the province was reviewed. The two DIRs use two different methods for patient matching. One uses a deterministic match based on one specific value, while the other uses a probabilistic scorecard that weighs a variety of patient demographics to assess if the patients are a match. An analysis was conducted to measure how a patient identity domain that uses a deterministic approach would compare to the accepted "standard." The intention is to review the analysis as a means of identifying interesting insights in both approaches. For the purpose of this paper, the two DIRs will be referred to as DIR1 and DIR2.

A Novel Morphological Analysis of DXA-DICOM Images by Artificial Neural Networks for Estimating Bone Mineral Density in Health and Disease.

One of the best methods for diagnosing bone disease in humans is site-specific and total bone mineral density (BMD) measurements by Dual-energy X-ray Absorptiometry (DXA) machines. The basic disadvantage of this technology is inconsistent BMD measurements among different DXA machines from different manufacturers due to different image analysis algorithms. The objective of the present study was to apply artificial neural networks (ANNs) to estimate total BMD for diagnosing a population of Egyptians with and without pathology, using extracted features from DXA-DICOM images based on the Histogram and Binary algorithms as compared to reference BMD measurements by DXA machine. The sample size comprised 3000 male and female participants with an age range 22-49 years, who were referred to us for diagnosis and/or treatment and for DXA total body scans in the period from January 2016 till December 2017. We constructed an entry computer data-logging visible unit, where we applied morphological operations to get a specific bone image, and used their extracted feature vectors as inputs to ANNs with cascade training, gathering, and testing for DXA-DICOM image processing. The multilayer feed-forward ANN set up its initial weights, carried out training and initiated the recall mode, and finally observed its decision and interaction based on estimated BMD. The ANN construction was carried out using a 3-layer architecture, with one hidden layer of 85 neurons. The input layer has neuron numbers equal to 256 for the Histogram and 77,365 for Binary algorithms, respectively. Total BMD estimation performance based on the Binary algorithm was capable of identifying all DXA-DICOM images with an accuracy of 100% for the training, cross-validation, and testing of the ANN phases. We believe this strategy will represent the means for standardizing bone measurements of all DXA machines, regardless of the manufacturer.

Measuring the Benefits of a Regional Imaging Environment.

The use of central diagnostic imaging repositories (DIRs), that allow separate organizations with disparate PACS systems to seamlessly share patient data, is becoming more common; and as a result, the documentation of measurable benefits is a key deliverable to all stakeholders. Central DIRs and the implementation of foreign exam management (FEM) provide clinical users with the ability to seamlessly access DI exams and reports that originate from an outside location. FEM has been implemented to varying degrees across regional DIRs within Canada [1]. Historically, measuring the benefits of transitioning from a film-based environment to a PACS environment has been documented as being difficult and poses challenges [2]. Many of these same challenges are exacerbated when trying to measure benefits across a regional DIR. From the DIR, it is easy to report on the overall number of foreign exams that were transferred from the DIR to each individual site. While this metric does provide some insight into the number of patients migrating between hospitals and clinics, and demonstrates a growth pattern of the ingestion of foreign exams, it does not provide insight into the use and value of these foreign exams to the clinical user. At the outset, we hypothesized that quantifiable benefits could be measured, but would likely yield understated measurable results, due to the complexities involved in gathering data. In spite of this challenge, with targeted analysis across the region, together with many qualitative results from clinical users, a compelling picture would emerge.

Technical Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper.

This white paper explores the technical challenges and solutions for acquiring (capturing) and managing enterprise images, particularly those involving visible light applications. The types of acquisition devices used for various general-purpose photography and specialized applications including dermatology, endoscopy, and anatomic pathology are reviewed. The formats and standards used, and the associated metadata requirements and communication protocols for transfer and workflow are considered. Particular emphasis is placed on the importance of metadata capture in both order- and encounter-based workflow. The benefits of using DICOM to provide a standard means of recording and accessing both metadata and image and video data are considered, as is the role of IHE and FHIR.

Workflow Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper.

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient's electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges.

The Current State and Path Forward For Enterprise Image Viewing: HIMSS-SIIM Collaborative White Paper.

Clinical specialties have widely varied needs for diagnostic image interpretation, and clinical image and video image consumption. Enterprise viewers are being deployed as part of electronic health record implementations to present the broad spectrum of clinical imaging and multimedia content created in routine medical practice today. This white paper will describe the enterprise viewer use cases, drivers of recent growth, technical considerations, functionality differences between enterprise and specialty viewers, and likely future states. This white paper is aimed at CMIOs and CIOs interested in optimizing the image-enablement of their electronic health record or those who may be struggling with the many clinical image viewers their enterprises may employ today.

Enterprise Imaging Governance: HIMSS-SIIM Collaborative White Paper.

Enterprise imaging governance is an emerging need in health enterprises today. This white paper highlights the decision-making body, framework, and process for optimal enterprise imaging governance inclusive of five areas of focus: program governance, technology governance, information governance, clinical governance, and financial governance. It outlines relevant parallels and differences when forming or optimizing imaging governance as compared with other established broad horizontal governance groups, such as for the electronic health record. It is intended for CMIOs and health informatics leaders looking to grow and govern a program to optimally capture, store, index, distribute, view, exchange, and analyze the images of their enterprise.

A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper.

Care providers today routinely obtain valuable clinical multimedia with mobile devices, scope cameras, ultrasound, and many other modalities at the point of care. Image capture and storage workflows may be heterogeneous across an enterprise, and as a result, they often are not well incorporated in the electronic health record. Enterprise Imaging refers to a set of strategies, initiatives, and workflows implemented across a healthcare enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all clinical imaging and multimedia content to enhance the electronic health record. This paper is intended to introduce Enterprise Imaging as an important initiative to clinical and informatics leadership, and outline its key elements of governance, strategy, infrastructure, common multimedia content, acquisition workflows, enterprise image viewers, and image exchange services.

Open-Source, Web-Based Dashboard Components for DICOM Connectivity.

The administration of a DICOM network within an imaging healthcare institution requires tools that allow for monitoring of connectivity and availability for adequate uptime measurements and help guide technology management strategies. We present the implementation of an open-source widget for the Dashing framework that provides basic dashboard functionality allowing for monitoring of a DICOM network using network "ping" and DICOM "C-ECHO" operations.

Anatomy of an Extensible Open Source PACS.

The conception and deployment of cost effective Picture Archiving and Communication Systems (PACS) is a concern for small to medium medical imaging facilities, research environments, and developing countries' healthcare institutions. Financial constraints and the specificity of these scenarios contribute to a low adoption rate of PACS in those environments. Furthermore, with the advent of ubiquitous computing and new initiatives to improve healthcare information technologies and data sharing, such as IHE and XDS-i, a PACS must adapt quickly to changes. This paper describes Dicoogle, a software framework that enables developers and researchers to quickly prototype and deploy new functionality taking advantage of the embedded Digital Imaging and Communications in Medicine (DICOM) services. This full-fledged implementation of a PACS archive is very amenable to extension due to its plugin-based architecture and out-of-the-box functionality, which enables the exploration of large DICOM datasets and associated metadata. These characteristics make the proposed solution very interesting for prototyping, experimentation, and bridging functionality with deployed applications. Besides being an advanced mechanism for data discovery and retrieval based on DICOM object indexing, it enables the detection of inconsistencies in an institution's data and processes. Several use cases have benefited from this approach such as radiation dosage monitoring, Content-Based Image Retrieval (CBIR), and the use of the framework as support for classes targeting software engineering for clinical contexts.